Energy efficient prediction clustering algorithm for multilevel heterogeneous wireless sensor networks

multilevel heterogeneous wireless sensor networks Tang Liu Jian Peng Jin Yang Chunli Wang College of Fundamental Education, Sichuan Normal University, Chengdu 610068, China State key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China College of Computer Science, Sichuan University, Chengdu 610065, China Department of Computer Science, Leshan Normal University. Leshan 614000, China e English Department, Chengdu University of Information Technology, Chengdu 610225, China Abstract: In designing wireless sensor networks, it is important to reduce energy dissipation and prolong network lifetime. In this paper, a new model with energy and monitored objects heterogeneity is proposed for heterogeneous wireless sensor networks. We put forward an energy-efficient prediction clustering algorithm, which is adaptive to the heterogeneous model. This algorithm enables the nodes to select the cluster head according to factors such as energy and communication cost, thus the nodes with higher residual energy have higher probability to become a cluster head than those with lower residual energy, so that the network energy can be dissipated uniformly. In order to reduce energy consumption when broadcasting in clustering phase and prolong network lifetime, an energy consumption prediction model is established for regular data acquisition nodes. Simulation results show that compared with current clustering algorithms, this algorithm can achieve longer sensor network lifetime, higher energy efficiency and superior network monitoring quality.